Wavelet analysis on the variability, teleconnectivity, and predictability of the seasonal rainfall of Taiwan

Using wavelet analysis, the variability and oscillations of November-January (NDJ) and January-March (JFM) rainfall (1974-2006) of Taiwan and seasonal sea surface temperature (SST) of the Pacific Ocean were analyzed. From the scale-average wavelet power (SAWP) computed for the seasonal rainfall, it seems that the data exhibit interannual oscillations at a 2-4-yr period. On the basis of correlation fields between decadal component removed wavelet PC (DCR-WPC1) of seasonal rainfall and decadal component removed scale-averaged wavelet power (DCR-SAWP) of SST of Pacific Ocean at one-season lead time, SST of some domains of the western Pacific Ocean (July-September SST around 08-308N, 1208-1608E; October-December SST around 08-608N, 1258E-1608W) were selected as predictors to predict seasonal NDJ and JFM rainfall of Taiwan at one-season lead time, respectively, using an Artificial Neural Network calibrated by the Genetic Algorithm (ANN-GA). The ANN-GA was first calibrated using the 1975-99 data and independently validated using 2000-06 data. In terms of summary statistics such as the correlation coefficient, root-mean-square error (RMSE), and Hanssen-Kuipers (HK) scores, the prediction of seasonal rainfall of northern and western Taiwan using ANN-GA are generally good for both calibration and validation stages, but not so for south-eastern Taiwan because the seasonal rainfall of the former are much more significantly correlated to the SST of selected sectors of the Pacific Ocean than the latter.